Conventionally, an electronic scanning type radar has been used as a radar mounted on a vehicle, which utilizes the system of an FMCW (Frequency Modulated Continuous Wave) radar, a multifrequency CW (Continuous Wave) radar, a pulse radar, and the like.
According to each of the radars mentioned above, an estimation method of a direction of an incoming wave of an array antenna is used as a technology for detecting a direction of an incoming wave (or a reception wave) from a target.
An example of this estimation method of a direction of an incoming wave is a null operation method (refer to Non-Patent Document 1 and 2, for instance) known as a super-resolution (high precision) algorithm such as a beam scanning method like the Beamformer Method and the Capon Method, a linear prediction method like the Maximum Entropy Method (MEM: Maximum Entropy Method), the Minimum Norm Method, MUSIC (Multiple Signal Classification) Method, and the ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) Method.
Further, the estimation of the direction of the incoming wave, used by a radar mounted on a vehicle, is conducted by a method (refer to, for example, Patent Document 1) using solely the digital beam forming (DBF: Digital Beam Forming) of the Beamformer Method, or by a method (refer to, for example, Patent Documents 2 and 3) combining the DBF and the Maximum Entropy Method in recent years in order to enhance the detection accuracy of the arrival direction of the reception wave (or the resolution performance of the target).
Moreover, in order to apply a super-resolution algorithm such as MUSIC to a vehicle-mounted radar, a microprocessor is used which is utilized for vehicle-mounted radar with a low arithmetic processing capacity, compared to normal personal computers. Therefore, a method is developed by simplifying an estimation algorithm (refer to, for example, Patent Documents 4, 5, 6, and 7).
According to the super-resolution algorithm such as MUSIC, mentioned above, a correlation matrix is generated from data of reception waves received by each array antenna. An eigen value calculation is conducted by this correlation matrix. Thus, the arrival direction of the reception wave is detected. Here, the eigen value calculation (computation) refers to a calculation obtaining the eigen value and the eigenvector.
At this time, the super-resolution algorithm generates a correlation matrix by an ensemble average of the reception data because the accuracy of detecting the arrival direction enhances as the noise element of the correlation matrix is removed.
For example, the FMCW radar uses a correlation matrix generated by obtaining highest possible number of samples of a data set of the received beat signal (a temporal sequence data of a certain period of time, which can be converted to a data in a frequency domain) and obtaining an average. The number of samples is called a snapshot number (refer to, for example, Non-Patent Documents 1 and 2).
However, according to the vehicle-mounted radar, the distance from the target and the relative velocity change constantly. Therefore, even if the snapshot number is increased nebulously, the accuracy of detecting the arrival direction of the reception wave does not necessarily increase.
On the contrary, in order to increase the snapshot number within a control cycle (or a detection cycle such as 100 ms) for detecting the target, the frequency resolution operation corresponding to the reception signal must be conducted simultaneously for a number of times multiplying the number of array antennas and the snapshot number. Thus, there is a limit on the number.
Patent Document 4 describes a method in order to enhance the accuracy of detecting the target without increasing the snapshot number. According to this method described in Patent Document 4, for example, a correlation matrix is stored for each beat frequency at the previous control cycle (or the control cycle immediately prior to the previous control cycle). Further, this method obtains a weighted sum (weighted average) of a correlation matrix of a beat frequency, for which a target exists in the present control cycle, and a correlation matrix in the previous control cycle (or the control cycle immediately prior to the previous control cycle) having the same beat frequency. Moreover, Patent Document 4 describes a method which stores the weighted averaged correlation matrix for each beat frequency, and obtains a further weighted sum of the correlation matrix of a beat frequency for which a target exists in the present control cycle and a correlation matrix with the same frequency obtained as described above by weighted summation.
In addition, Patent Document 5, mentioned above, discloses a method for increasing the snapshot number by averaging each correlation matrix generated for a frequency indicating a peak value, from among the same peak waveform for which a target exists at a beat frequency, and for a frequency in the vicinity (for example, ± two frequency resolutions).
According to this Patent Document 5, an average of the correlation matrix in the frequency domain is further averaged by using a past correlation matrix.
Next, Patent Document 6 describes estimating an arrival direction of the reception wave by combining a method estimating the form of a roadside and a method averaging the present and past correlation matrices.
In addition, Patent Document 7 describes a method which determines a weighted coefficient (or a forgetting coefficient: a constant representing the degree of forgetting) in real time in order to average the present and past correlation matrices.
Further, an electronic scanning type radar has been used conventionally as a radar mounted on a vehicle, which utilizes the system of an FMCW (Frequency Modulated Continuous Wave) radar, a multifrequency CW (Continuous Wave) radar, a pulse radar, and the like.
According to each of the radars mentioned above, an estimation method of a direction of an incoming wave of an array antenna is used as a technology for detecting a direction of an incoming wave (or a reception wave) which is a reflection wave from a target corresponding to a transmission wave.
An example of this estimation method of a direction of an incoming wave is a null operation method (refer to Non-Patent Document 1 and 2, for instance) known as a super-resolution (high precision) algorithm such as a beam scanning method like the Beamformer Method and the Capon Method, a linear prediction method like the Maximum Entropy Method (MEM: Maximum Entropy Method), the Minimum Norm Method, MUSIC (Multiple Signal Classification) Method, and the ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) Method.
Further, the estimation of the direction of the incoming wave, used by a radar mounted on a vehicle, is conducted by a method (refer to, for example, Patent Document 1) using solely the digital beam forming (DBF: Digital Beam Forming) of the Beamformer Method, or by a method (refer to, for example, Patent Documents 2 and 3) combining the DBF and the Maximum Entropy Method in recent years in order to enhance the detection accuracy of the arrival direction of the reception wave (or the resolution performance of the target).
Moreover, in order to apply a super-resolution algorithm such as MUSIC to a vehicle-mounted radar, methods have been developed with an inclination to simplify the processing (refer to, for example, Patent Documents 4 and 8). These methods can be applied to a vehicle-mounted device which has a low processing capacity compared to normal personal computers.
According to the super-resolution algorithm such as MUSIC, mentioned above, it is preferred that the direction of the incoming wave is estimated after a number of the incoming waves is estimated, in order to enhance the accuracy with which the direction is estimated.
Non-Patent Documents 1 and 2 introduce the AIC (Akaike Information Criteria) and the MDL (Minimum Description Length) as methods to estimate the number of incoming waves based on the maximum-likelihood approach in statistical processing.
However, according to the estimation methods introduced in Non-Patent Documents 1 and 2, it is necessary to collect a large amount of data and then conduct a dispersion assessment. Therefore, it is not suitable to use these methods for a vehicle-mounted radar whose relative distance to the target and relative velocity fluctuate rapidly.
Patent Document 8 describes a method for estimating the number of incoming waves, necessary for computing the MUSIC spectrum, with a light computation load. In other words, Patent Document 8 describes a method which is an application of a threshold approach in which an eigen value is computed, and the signal space and a noise space are estimated individually based on the magnitude of the eigen value.
In this case, the reception intensity of the radar declines as the measured distance becomes larger. Therefore, the method described above estimates the number of incoming waves by storing and setting a threshold for each relative distance to the target, and then comparing this threshold with the eigen value (equivalent to the reception intensity).
In addition, there is a method (refer to Patent Documents 9, for example) which normalizes an eigen value as one of the diagonal element value of the original covariance matrix (in other words, the correlation matrix), and then distinguishes based on a threshold value. This method is not designed to be used for a vehicle-mounted device.
Furthermore, an estimation method which estimates the number of incoming waves with a high degree of accuracy constantly performs a spectrum computation assuming that the number of incoming waves is the maximum number that can be received, and in the subsequent calculation of electronic power, unnecessary peak values are removed, and obtain a final estimation value of the number of incoming waves (Patent Document 10).
Moreover, an electronic scanning type radar has been conventionally used as a radar mounted on a vehicle, which utilizes the system of an FMCW (Frequency Modulated Continuous Wave) radar, a multifrequency CW (Continuous Wave) radar, a pulse radar, and the like.
According to each of the radars mentioned above, an estimation method of a direction of an incoming wave of an array antenna is used as a technology for detecting a direction of an incoming wave (or a reception wave) which is a reflection wave from a target corresponding to a transmission wave.
An example of this estimation method of a direction of an incoming wave is a null operation method (refer to Non-Patent Document 1 and 2, for instance) known as a super-resolution (high precision) algorithm such as a beam scanning method like the Beamformer Method and the Capon Method, a linear prediction method like the Maximum Entropy Method (MEM: Maximum Entropy Method), the Minimum Norm Method, MUSIC (Multiple Signal Classification) Method, and the ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) Method.
Further, the estimation of the direction of the incoming wave, used by a radar mounted on a vehicle, is conducted by a method (refer to, for example, Patent Document 1) using solely the digital beam forming (DBF: Digital Beam Forming) of the Beamformer Method, or by a method (refer to, for example, Patent Documents 2 and 3) combining the DBF and the Maximum Entropy Method in recent years in order to enhance the detection accuracy of the arrival direction of the reception wave (or the resolution performance of the target).
Moreover, in order to apply a super-resolution algorithm such as MUSIC to a vehicle-mounted radar, methods have been developed with an inclination to simplify the processing (refer to, for example, Patent Documents 4 and 8). These methods can be applied to a vehicle-mounted device which has a low processing capacity compared to normal personal computers.
According to the super-resolution algorithm such as MUSIC, mentioned above, it is preferred that the direction of the incoming wave is estimated after a number of the incoming waves is estimated, in order to enhance the accuracy with which the direction is estimated.
Non-Patent Documents 1 and 2 introduce the AIC (Akaike Information Criteria) and the MDL (Minimum Description Length) as methods to estimate the number of incoming waves based on the maximum-likelihood approach in statistical processing.
However, according to the estimation methods introduced in Non-Patent Documents 1 and 2, it is necessary to collect a large amount of data and then conduct a dispersion assessment. Therefore, it is not suitable to use these methods for a vehicle-mounted radar whose relative distance to the target and relative velocity fluctuate rapidly.
Patent Document 8 describes a method for estimating the number of incoming waves, necessary for computing the MUSIC spectrum, with a light computation load. In other words, Patent Document 8 describes a method which is an application of a threshold approach in which an eigen value is computed, and the signal space and a noise space are estimated individually based on the magnitude of the eigen value.
In this case, the reception intensity of the radar declines as the measured distance becomes larger. Therefore, the method described above estimates the number of incoming waves by storing and setting a threshold for each relative distance to the target, and then comparing this threshold with the eigen value (which is proportional to the reception intensity).
In addition, there is a method (refer to Patent Documents 9, for example) which normalizes an eigen value as one of the diagonal element value of the original covariance matrix (in other words, the correlation matrix), and then distinguishes based on a threshold value. This method is not designed to be used for a vehicle-mounted device.    [Non-Patent Document 1] “Adaptive Signal Processing Using Array Antennas,” Kikuma Nobuyoshi (Kagaku Gijutsu Shyuppan, 1998).    [Non-Patent Document 2] “Adaptive Antenna Technology,” Kikuma Nobuyoshi (Ohm Sha, 2003).    [Patent Document 1] Japanese Unexamined Patent Application, First Publication No. 2000-284044    [Patent Document 2] Japanese Unexamined Patent Application, First Publication No. 2006-275840    [Patent Document 3] Japanese Unexamined Patent Application, First Publication No. 2006-308542    [Patent Document 4] Japanese Unexamined Patent Application, First Publication No. 2007-040806    [Patent Document 5] Japanese Unexamined Patent Application, First Publication No. 2006-145251    [Patent Document 6] Japanese Unexamined Patent Application, First Publication No. 2006-242695    [Patent Document 7] Japanese Unexamined Patent Application, First Publication No. 2006-284182    [Patent Document 8] Japanese Unexamined Patent Application, First Publication No. 2006-047282    [Patent Document 9] Japanese Unexamined Patent Application, First Publication No. 2006-153579    [Patent Document 10] Japanese Unexamined Patent Application, First Publication No. 2000-121716